Influence maximization is the problem of trying to maximize the number of influenced nodes by selecting optimal seed nodes, given that influencing these nodes is costly. Due to the probabilistic nature of the problem,...
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This paper proposes a stochastic multistage expansion planning method in order to solve the mid-term and long-term optimal feeder routing problem. Pseudo dynamic behaviour of the network parameters and geographical co...
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This paper proposes a stochastic multistage expansion planning method in order to solve the mid-term and long-term optimal feeder routing problem. Pseudo dynamic behaviour of the network parameters and geographical constraints besides the associated uncertainties of future load demand and market price are incorporated into the method. The proposed method is solved from the DisCo viewpoints using particle swarm optimization algorithm which finally converges to a solution with minimum costs. The proposed cost function is the sum of feeder's installation costs, power losses cost, cost of active purchased power from power market, and reliability costs. Meanwhile, the final solution must satisfy all the operation aspects of power system in acceptable levels. On the other hand, the implementation of the final strategy obtained from the proposed method of this paper, can increase the responsibility of the power system in lower costs. In this regard, distribution system can power its customers with higher power quality in acceptable reliability level and also in lower costs. The proposed method is applied on a large-scale distribution network and its practicability and also its effectiveness are analysed due to the simulation results.
This paper basically concentrates on the impact of a price-maker energy storage unit (PMESU) on the operation of the power system. Recently, energy storage systems (ESSs) have attracted much attention as one of the re...
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This paper basically concentrates on the impact of a price-maker energy storage unit (PMESU) on the operation of the power system. Recently, energy storage systems (ESSs) have attracted much attention as one of the renewable and pollution-free sources of energy and are applicable due to their exclusive advantages in power systems. In this paper, the ESS considered as a price-maker market player, which attempts to maximize the benefits of its competition in the market environment. The main operation problem of the PMESU represented as a bi-level optimization problem which comprises of nonlinear terms. First, a stochastic-based bidding/offering approach (SBOA) is considered which is less complex and takes lesser time to be solved compared to the MPEC method. Second, conditional Value-at-Risk (CVaR) method is used in order to control the risk of utilizing inoperative scenarios. Finally, the operation problem of the proposed PMESU is formed as a max-min problem in order to analyse the impact of the PMESU in the worst case condition from the perspective of the independent system operator (ISO). In addition, transmission switching (TS) method is utilized to minimize the system operation cost from the ISO point of view. The performance of the proposed method has been evaluated on an IEEE 9-bus test system which shows the authenticity and validity of this work. (C) 2019 Published by Elsevier Ltd.
A multi-stage model predictive control approach is proposed to compensate the forecast error in a scenariobased two-stage stochastic dynamic economic dispatch problem through a feedback mechanism. Reformulating the p...
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A multi-stage model predictive control approach is proposed to compensate the forecast error in a scenariobased two-stage stochastic dynamic economic dispatch problem through a feedback mechanism. Reformulating the problem as a finite moving-horizon optimal control problem, the proposed approach decelerates the growth of the number of scenarios by updating the system as uncertainties are gradually realized. Consequently, the computation time is reduced, and the problem is solved without the need for using scenario reduction techniques that compromise the accuracy of the solution. To exhibit the computational efficiency of the proposed approach, numerical experiments are conducted on the IEEE 118-bus system. (C) 2017 Published by Elsevier Ltd.
This paper evaluates the optimal bidding strategy for demand response (DR) aggregator in day-ahead (DA) markets. Because of constraint of minimum power quantity requirement, small-sized customers have to become indire...
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ISBN:
(纸本)9781467366922
This paper evaluates the optimal bidding strategy for demand response (DR) aggregator in day-ahead (DA) markets. Because of constraint of minimum power quantity requirement, small-sized customers have to become indirect participants of electricity markets via the DR aggregator, who could offer various contracts accessing customers' demand reduction capacity in advance. In day-ahead markets, DR aggregator schedules those contracts and submits accumulated DR offers to the system operator. The objective is to maximize the profit of the DR aggregator. The key element affecting the bidding decision and aggregator's profit is the uncertain hourly DA prices. The stochasticprogramming adopts scenario-based approach for helping the profit-seeking DR aggregator control uncertainties. Robust optimization employs forecast values with bounded price intervals to address uncertainties while adjusting the robustness of the solution flexibly. Both scenarios can be modelled as mixed-integer linear programming (MILP) problems which could be solved by available solvers.
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